canthesebejited.py

masks = np.asarray([cv2.UMat(255 * np.ones((images[i].shape[0], images[i].shape[1]), np.uint8)) for i in range(img_names.shape[0])])
corners = np.asarray([warper.warp(images[idx], Kseam_work_aspect(cameras[idx].K().astype(np.float32),seam_work_aspect), cameras[idx].R, cv2.INTER_LINEAR, cv2.BORDER_REFLECT)[0] for idx in range(img_names.shape[0])])
masks_warped = np.asarray([warper.warp(masks[idx], Kseam_work_aspect(cameras[idx].K().astype(np.float32),seam_work_aspect), cameras[idx].R, cv2.INTER_NEAREST, cv2.BORDER_CONSTANT)[1].get() for idx in range(img_names.shape[0])]) 
images_warped = np.asarray([warper.warp(images[idx], Kseam_work_aspect(cameras[idx].K().astype(np.float32),seam_work_aspect), cameras[idx].R, cv2.INTER_LINEAR, cv2.BORDER_REFLECT)[1]for idx in range(img_names.shape[0])])
sizes = np.asarray([warper.warp(images[idx], Kseam_work_aspect(cameras[idx].K().astype(np.float32),seam_work_aspect), cameras[idx].R, cv2.INTER_LINEAR, cv2.BORDER_REFLECT)[1].shape[1::-1] for idx in range(img_names.shape[0])])
images_warped_f = np.asarray([img.astype(np.float32) for img in images_warped])